大面积遥感植被成图方法的述评

自然植被的地理空间分布是十分重要的环境因子，其应用需求范围很广，包括全球气候变化，自然灾害监测，生态系统活力监测以及火灾管理等。日益增长的更高层次的应用需要一种有一定比例尺的大范围的且能提供详细信息的植被数据集。笔者介绍一种能生成分辨率为30m的自然植被类型覆盖图以及可用于可燃物和火险评估项目的结构变量的遥感方法。此方法的成功有赖于传感器的改进和数据的质量、对区域及其植被生态的全面了解、大量遥感和地面数据的成功整合以及灵活的成图算法。初步成果来自犹他州中部地区，包括28个植被类型。森林、灌木和草本层的郁闭度（亚像元密度）的分类总精度为60％（按生命表平均），三者郁闭度的相关系数分别为0．89，0．60和0．55，平均冠层高的相关系数分别为0．73，0．50和0．20。对改进过的第一轮技术成果进行了讨论，其中包括成图模型的细化、有关环境梯度的应用以及与实际植被类型相关的潜在植被问题等。

Large Vegetation Mapping:A Methodology Review

Geospatial distribution of natural vegetation is among some of very important environmental parameters required for applications ranging from global climate change to monitoring of natural hazards, monitoring of ecosystem vitality, and fire management practices. Increasingly sophisticated applications require vegetation datasets to cover large areas at a suitable scale and provide sufficiently detailed information. In this paper, we describe a research effort to develop a remote sensing methodology capable of producing 30 m resolution, wall-to-wall coverage of natural vegetation types and structure variables in support of a multi-agency fire fuels and fire risks assessment project. Success of this remote sensing research effort is dependent on improved sensor and data qualities, a thorough understanding of regional and local vegetation ecology, successful integration of remote sensing with large amount of field plot data, and flexible mapping algorithms. Preliminary results produced in Wasatch Range and Uinta Mountains of central Utah include 28 vegetation types with an overall accuraccy of 60%(average by life forms), percent canopy density (sub-pixel density) of forest, shrub, and herbaceous cover, and average top canopy height of forest, shrub, and herbaceous cover. Techniques to improve the first-round results are discussed, including refinements of mapping models and use of relevant environmental gradients and potential vegetation classification associated with actual vegetation types.